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dbt-mcp

dbt-labs/dbt-mcp

A MCP server for interacting with dbt

GraphCanon updated 3w · GitHub synced 3w

595 stars128 forksLast push 1mo Python Apache-2.0

Decision brief

dbt-mcp offers a specialized MCP server for dbt interaction, supporting data analytics and engineering tasks in Python under the Apache-2.0 license.

Good fit when

  • You are working with dbt and need to implement the Model Context Protocol (MCP) for more precise project management.
  • Your team prefers Python as the programming language for deploying MCP servers connected to dbt projects.

Avoid when

  • If your project does not require interaction through the Model Context Protocol with dbt, opting for a generic data processing tool might be simpler and less resource-intensive.
  • When the primary goal is model training or prediction without focusing on dbt workflow management via MCP, alternative tools may serve better.
Pricing:
freemium - Freely available with no direct cost, ideal for projects without budget constraints but seeking flexibility.
Requirements:
Ensure you have Python and dbt installed to utilize dbt-mcp effectively.; Advanced knowledge of data engineering and analytics best practices is required to make the most out of dbt-mcp's MCP capabilities.

Observed Jul 16, 2026 · Source: enrich:decision_facts

Verify the decision

Maintenance and security

Full trust report
Maintenance
Very active (2d since push)
As of 3w
Provenance
Not a fork · Organization account
As of 3w
Security (OSV)
No MCP manifest
As of 1mo

Public GitHub metadata and optional OSV scans. Signals, not a guarantee. Trust methodology.

Install

pip install dbt-mcp
PyPI

Similar tools

Same-category neighbours. No typed graph edges are catalogued for this tool yet.

Evidence and technical details

Sourced facts, taxonomy, compatibility claims, README excerpt, and machine-readable endpoints.

Overview

dbt-labs/dbt-mcp provides an implementation of the Model Context Protocol (MCP) server designed to facilitate interactions with dbt.

Capability facts

Deploy
Self-host

Source: dockerfile:Dockerfile · Jul 27, 2026

Docker
Dockerfile present

Source: dockerfile:Dockerfile · Jul 27, 2026

CLI
CLI entrypoint

Source: pyproject.toml:[project.scripts] · Jul 27, 2026

Languages
python

Source: github.language+pyproject.toml · Jul 27, 2026

Categories

Tags

README

dbt MCP Server

This MCP (Model Context Protocol) server provides various tools to interact with dbt. You can use this MCP server to provide AI agents with context of your project in dbt Core, dbt Fusion, and dbt Platform.

Read our documentation here to learn more. This blog post provides more details for what is possible with the dbt MCP server.

Experimental MCP Bundle

We publish an experimental Model Context Protocol Bundle (dbt-mcp.mcpb) with each release so that MCPB-aware clients can import this server without additional setup. Download the bundle from the latest release assets and follow Anthropic's mcpb CLI docs to install or inspect it.

Feedback

If you have comments or questions, create a GitHub Issue or join us in the community Slack in the #tools-dbt-mcp channel.

Architecture

The dbt MCP server architecture allows for your agent to connect to a variety of tools.

Tools

SQL

Tools for executing and generating SQL on dbt Platform infrastructure.

  • execute_sql: Executes SQL on dbt Platform infrastructure with Semantic Layer support.
  • text_to_sql: Generates SQL from natural language using project context.

Semantic Layer

To learn more about the dbt Semantic Layer, click here.

  • get_dimension_values: Gets distinct values for a dimension; option to scope to specific metrics.
  • get_dimensions: Gets dimensions for specified metrics.
  • get_entities: Gets entities for specified metrics.
  • get_metrics_compiled_sql: Returns compiled SQL for metrics without executing the query.
  • list_metrics: Retrieves all defined metrics.
  • list_saved_queries: Retrieves all saved queries.
  • query_metrics: Executes metric queries with filtering and grouping options.

Discovery

To learn more about the dbt Discovery API, click here.

  • get_all_macros: Retrieves macros; option to filter by package or return package names only.
  • get_all_models: Retrieves name and description of all models.
  • get_all_sources: Gets all sources with freshness status; option to filter by source name.
  • get_exposure_details: (deprecated — use get_node_details instead)
  • get_exposures: Gets all exposures (downstream dashboards, apps, or analyses).
  • get_lineage: Gets full lineage graph (ancestors and descendants) with type and depth filtering.
  • get_macro_details: (deprecated — use get_node_details instead)
  • get_mart_models: Retrieves all mart models.
  • get_model_children: (deprecated — use get_lineage instead)
  • get_model_details: (deprecated — use get_node_details instead)
  • get_model_health: Gets health signals: run status, test results, and upstream source freshness.
  • get_model_parents: (deprecated — use get_lineage instead)
  • get_model_performance: Gets execution history for a model; option to include test results.
  • get_node_details: Gets full details for any dbt resource type (model, source, exposure, test, seed, snapshot, macro, semantic_model).
  • get_related_models: Finds similar models using semantic search.
  • get_seed_details: (deprecated — use get_node_details instead)
  • get_semantic_model_details: (deprecated — use get_node_details instead)
  • get_snapshot_details: (deprecated — use get_node_details instead)
  • get_source_details: (deprecated — use get_node_details instead)
  • get_test_details: (deprecated — use get_node_details instead)
  • search: [Alpha] Searches for resources across the dbt project (not generally available).

dbt CLI

Allowing your client to utilize dbt commands through the MCP tooling could modify your data models, sources, and warehouse objects. Proceed only if yo

For agents

This page has a .md twin and JSON over the API.

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